Payment Service Risk Metric Merchant Filtering
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Solution Overview
Problem
Conventional search techniques fail to generate search results based on transaction data associated with payment services, leading to wasted computing resources and unsuccessful transactions due to the presentation of unfeasible merchant options to users.
Innovation Solution
A system that utilizes transaction data and interaction data to intelligently generate search results, ranking entities based on risk and relevance metrics, and dynamically modifies permissions and interactions to ensure users can successfully complete transactions by prioritizing low-risk entities and adjusting payment options and transaction limits accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search techniques are used to present merchant options to users, then search results are generated quickly, but many presented merchants are unfeasible leading to unsuccessful transactions and wasted computing resources
Solution Approach 1:
The system performs preliminary risk assessment and feasibility analysis on merchants before presenting them in search results. Transaction data is analyzed in advance to identify feasible merchants, ensuring that only promising options are presented to users, thereby avoiding wasted computing resources on unsuccessful transactions.
Solution Approach 2:
The system uses feedback from transaction data to continuously refine search result generation. By analyzing historical transaction outcomes, the system learns which merchants are feasible and adjusts search result presentation accordingly, improving transaction success rates while optimizing resource utilization.
2Reliability
If risk metrics are incorporated into search result generation to improve transaction feasibility, then transaction success rate increases, but system complexity increases due to additional data processing requirements
Solution Approach 1:
The payment service system performs multiple functions using the same infrastructure: it processes payments, analyzes transaction data for risk assessment, and generates personalized search results. This multi-functionality allows risk-based search optimization without requiring separate dedicated systems, thereby managing complexity while improving transaction success rates.
3Productivity
If transaction data analysis is used to generate personalized search results, then user interaction efficiency improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of transaction data to pre-compute risk metrics and merchant feasibility scores before search queries are submitted. This advance preparation enables rapid generation of personalized search results when users submit queries, improving interaction efficiency without excessive processing delays.
Data Source
AI summary
Using risk metrics to facilitate transactions is described. A payment service computing platform may, in association with a request to initiate an interaction with a merchant, determine a user risk metric associated with a user of a payment service. The platform may determine, based on transaction data associated with merchants, merchant risk metrics associated with the merchants, filter the merchants based on the user risk metric and the merchant risk metrics to generate a list of merchants, and cause a user interface of a payment application associated with the payment service and executing on a device of the user to present at least a portion of the list of merchants. The platform may then receive indications of a selected merchant and an item(s) to be purchased from the selected merchant, use a single-use payment instrument to complete a transaction for the item(s), and generate a loan associated with the transaction.